Model for the Analysis of Binary Longitudinal Pain Data Subject to Informative Dropout through Remedication

Abstract

We address the problem of accounting for informative dropout in the form of rescue medication when comparing pain relievers with respect to longitudinal binary pain-relief outcomes. We present a selection model approach for binary longitudinal data that accommodates informative dropout. The relationship between dropout or remedication and the binary pain-relief response is assumed to be characterized by a random effect. That is, conditional on this random effect, response and dropout are independent. Unlike previous approaches to this problem, which rely on numerical or approximation methods, we obtain a closed-form expression for the marginal log-likelihood of response and dropout by specifying a complementary log-log link function for both components and a conjugate log-gamma random effect distribution. A data analysis supported by simulation results suggest that the model fits reasonably well. Results are compared to those obtained from conventional, but somewhat inappropriate analyses.